Consequently, only relying on a single WEM would limit our sampling of words. For instance, word choice on social media is typically more conversational than language found in official write-ups ( 15, 16). We develop a threat dictionary to advance theory on the societal impact of mass communicated threats and to enable the analysis of these effects historically and in real time.Īn important measurement development goal of this project was to create a threat dictionary that generalized across different linguistic contexts. This involves scanning documents for the presence of select keywords representative of a construct of interest. To develop a semantic measure that tracks the form, frequency, and magnitude of communicated threats, we used a dictionary-based approach ( 11). This research specifically aims to 1) develop and validate a computationally derived measure to identify threat-related content using natural language processing (NLP) methods 2) track how fluctuations in threats have been changing over the past 100 years of US history and 3) examine how these changing threat levels offer predictive insights into America’s shifting cultural norms, political attitudes, and macroeconomic activity. To understand the implications of threats broadcasted through mass communication channels, such as the news or social media, the present study created a computational linguistic tool that indexes threat levels from texts. However, to date, our ability to detect threatening language in texts is limited by the lack of accessible, validated linguistic dictionaries. Whether the goal is to inform or exploit, claims about threats are pervasive in public discourse, and there is an urgent need for both researchers and policy makers to understand its social consequences. The rise of social media, in particular, has fueled research on the negative effects of fear mongering and the mass circulation of misinformation ( 7– 10). By raising the alarm on our impending doom, threat-related words and messages can instantly attract our attention-activating the fear circuitry in the human brain that collates and accentuates valuable survival information ( 5, 6).
Linguistic dictionaries torrent#
Especially within the vast digital landscape, we absorb a torrent of online content on platforms, apps, and news feeds designed to elicit our fear of potential threats ( 4). In today’s world, ominous warnings about imminent threats are prevalent in advertisements, political rhetoric, and newscasts ( 1– 3). In all, the language of threats is a powerful tool that can inform researchers and policy makers on the public’s daily exposure to threatening language and make visible interesting societal patterns across American history. The data also showed that threatening language is contagious.
Using data from newspapers that span over 100 years, we found change in threats to be associated with tighter social norms and collectivistic values, stronger approval of sitting US presidents, greater ethnocentrism and conservatism, lower stock prices, and less innovation. Moreover, the dictionary offers predictive insights on US society’s shifting cultural norms, political attitudes, and macroeconomic activities. We demonstrate this measure’s convergent validity with objective threats in American history, including violent conflicts, natural disasters, and pathogen outbreaks such as the COVID-19 pandemic. To fill this gap, we developed a threat dictionary, a computationally derived linguistic tool that indexes threat levels from mass communication channels. However, to date, we have lacked the tools needed to identify threatening language and track its impact on human groups.
In today’s vast digital landscape, people are constantly exposed to threatening language, which attracts attention and activates the human brain’s fear circuitry.
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